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RDM Education with the Galaxy Training Network (GTN) - FAIR and scalable RDM training with Galaxy, GTN and TIaaS

Authors: Müller, Teresa; Hiltemann, Saskia;

RDM Education with the Galaxy Training Network (GTN) - FAIR and scalable RDM training with Galaxy, GTN and TIaaS

Abstract

The Galaxy Project [1][2] is a widely-used open-source user-friendly platform for data science research that offers an extensive suite of tools and services for analyzing and visualizing research data. The Galaxy Training Network (GTN; [3][4]) is an open educational resource (OER) that provides a huge collection of training material for researchers, developers and trainers. With 34 topics, 465 tutorials, 23 learning pathways and over 57000 unique visitors over the last 12 months, it provides a comprehensive and valuable educational resource. While the original focus of Galaxy and the GTN was on the life sciences, in recent years this has expanded to include such fields as climate research, digital humanities, material sciences, computational chemistry, AI and machine learning, and more. Furthermore, the GTN tutorials are not limited to data analyses within the Galaxy platform, but also offer many tutorials on foundational data science skills (e.g. programming and data visualisation with R and python, unix command line, git) as well as a dedicated FAIR data topic with tutorials focusing on general RDM skills and competencies. GTN has achieved a perfect FAIRChecker [5] score, showcasing its commitment to FAIR principles through automated infrastructure. The platform excels in participant engagement, with over 3,000+ feedback responses aggregated automatically to enhance training effectiveness. Learning pathways, curated by experts, offer coherent and progressive learning experiences suitable for self-learning or training events. With its open contribution model, the GTN allows communities to add and maintain their own tutorials, with experts from each domain joining the GTN editorial board in order to peer review the content of tutorials and make sure they stay in line with the latest state-of-the-art in their research domains. Contributions to the GTN are in markdown format and streamlined through web forms and automatic testing, allowing users to contribute without technical expertise, thereby increasing community participation. GTN recognizes contributors with detailed profile pages and extends this recognition to projects, institutions, and funders, highlighting their community impact. Community Pages provide real-time insights into growth and collaboration of each topic. All tutorials are designed to be suitable for both self-study, as well as re-use by educators, supporting both classroom and virtual learning settings. To support the technical requirements of teaching the GTN materials, Training Infrastructure as a Service (TIaaS) [6] offers educators free computational resources on Galaxy for their training events. The GTN communities host several learning events throughout the year, including the yearly Galaxy Training Academy (formerly "Smorgasbord") events, an annual Massive Open Online Course (MOOC), where learners from around the world can participate through self-paced learning with 24/7 online support from a global community of over 100 instructors. This event receives over 3000 registrations each year, and in 2025 the 5th edition of this event will be held. In summary, the Galaxy Training Network offers a technically advanced and user-friendly platform with a deep commitment to FAIR principles, community engagement, and innovative features, making it a valuable resource for RDM education.

Related Organizations
Keywords

Galaxy, Open Science, Galaxy Training Network, Education

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green